Optimizing the Architecture of a Quantum–Classical Hybrid Machine Learning Model for Forecasting Ozone Concentrations: Air Quality Management Tool for Houston, Texas

Optimizing the Architecture of a Quantum–Classical Hybrid Machine Learning Model for Forecasting Ozone Concentrations: Air Quality Management Tool for Houston, Texas

Keeping track of air quality is paramount to issue preemptive measures to mitigate adversarial effects on the population. This study introduces a new quantum–classical approach, combining a graph-based deep learning structure with a quantum neural network to predict ozone concentration up to 6 h ahe...

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Bibliographic Details
Main Authors: Victor Oliveira Santos, Paulo Alexandre Costa Rocha, Jesse Van Griensven Thé, Bahram Gharabaghi
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Atmosphere
Subjects:
air pollution
ozone
Houston
forecasting
quantum machine learning
quantum neural network
Online Access:https://www.mdpi.com/2073-4433/16/3/255
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